Despite genetic, morphological and experimental in vivo, data implying fixed abnormalities in patients with absence seizures, attempts to find highly consistent features in the 3-Hz spike-and-wave pattern recorded during sequential seizures from the same subject have been largely unsuccessful. We used a new data decomposition technique called Independent Component Analysis (ICA) to separate multiple spike-and-wave episodes in the EEG recorded from five subjects with absence seizures into multiple consistent components. Each component corresponded to a temporally-independent waveform and a fixed spatial distribution. Almost all components separated by the ICA algorithm had overlapping, largely frontal spatial distributions. The analysis unmasked 5-8 components from each subject that were consistently activated across all seizures, with no components detected that were selectively activated by one seizure and not another. The "spike" and "wave" features noted in the EEG of every subject were each separated by the ICA algorithm into two or more components. Other components were active only at the beginning of each seizure or were related to ongoing brain activity not directly related to the 3Hz spike-and-wave pattern. By contrast randomly selected spatial patterns used for data decomposition resulted in components that were uninformative, similar to simply changing the montage for viewing the EEG. Our results suggest that despite previously described variability in the raw EEG, certain highly specific spatial distributions of activation are reproducible across seizures. These may reflect ictal and non-ictal brain activity consistently activating the same group of neurons.
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